SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC

Magdalena Michalska

mmagamichalska@gmail.com
Lublin University of Technology, Department of Electronics and Information Technology (Poland)
http://orcid.org/0000-0002-0874-3285

Abstract

The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selection and classification was described. Application examples of binary and multiclass classification are given. The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.


Keywords:

dermatoscopic images, neural networks, melanoma, skin lesions

Adegun A., Viriri S.: Deep learning-based system for automatic melanoma detection. IEEE Access 8/2020, 7160-7172.
DOI: https://doi.org/10.1109/ACCESS.2019.2962812   Google Scholar

Alendar F. et al.: Clear definitions,simple terminology, no metaphoric terms. Expert RevDermatol 3/2008, 27–29.
DOI: https://doi.org/10.1586/17469872.3.1.27   Google Scholar

Argenziano G. et al.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Archives of Dermatology 134/1998, 1563–1570.
DOI: https://doi.org/10.1001/archderm.134.12.1563   Google Scholar

Argenziano G. et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. Journal of American Academy of Dermatology 48(5)/2003, 679–693.
  Google Scholar

Attia M. et al.: Skin melanoma segmentation using recurrent and convolutional neural networks. Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium, IEEE, 292–296.
DOI: https://doi.org/10.1109/ISBI.2017.7950522   Google Scholar

Blum H., Ellwanger U.: Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions, British Journal of Dermatology 151(5)/2004, 1029–1038.
DOI: https://doi.org/10.1111/j.1365-2133.2004.06210.x   Google Scholar

Brinker T.J. et al.: Deep learning outperformed 136 of 157 dermatologists in a head-to-head der moscopic melanoma image classification task. Eur J Cancer 113/2019, 47–54.
  Google Scholar

Codella N. et al.: Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. Machine Learning in Medical Imaging 2015, 118–126.
DOI: https://doi.org/10.1007/978-3-319-24888-2_15   Google Scholar

Codella N. et al.: Deep learning ensembles for melanoma recognition in dermoscopy images 2016, http://arxiv.org/abs/1610.04662 (accessed 4 October 2019).
  Google Scholar

Esteva A. et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542/2017, 115–118.
  Google Scholar

Esteva A.: Dermatologist-level classification of skin cancer with deep neural networks. Nat. Res. 542(7639)/2017, 115–118,.
DOI: https://doi.org/10.1038/nature21056   Google Scholar

Ge Y. et al.: Melanoma seg-mentation and classification in clinical images using deep learning. ICMLC 2018: Proceedings of the 2018 10th International Conference on Machine Learning and Computing 2018, 252–256.
DOI: https://doi.org/10.1145/3195106.3195164   Google Scholar

Ge Z. et al.: Skin disease recognition using deep saliency features and multimodal learning of dermoscopy and clinical images. Springer, Cham LNCS 10435/2017, 250–258.
DOI: https://doi.org/10.1007/978-3-319-66179-7_29   Google Scholar

Haenssle H.A. et al.: Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29/2018, 1836–1342.
DOI: https://doi.org/10.1093/annonc/mdy520   Google Scholar

Haenssle H.A. et al.: Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermato-logists working under less artificial conditions. Ann Oncol 31/2020, 137–143.
  Google Scholar

He K., Zhang X, Ren, S., Sun J.: Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition 2016, 770–778.
DOI: https://doi.org/10.1109/CVPR.2016.90   Google Scholar

Hekler A. et al.: Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur J Cancer 118/2019, 91–96.
DOI: https://doi.org/10.1016/j.ejca.2019.06.012   Google Scholar

Kittler H. et al.: Dermatoscopy of unpigmented lesions of the skin: A new classification of vessel morphology based on pattern analysis. Dermatopathology: Practical & Conceptual 14(4)/2008, 3.
  Google Scholar

Li Y., Shen L.: Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18/2018, 556.
DOI: https://doi.org/10.3390/s18020556   Google Scholar

Lopez A. R. et al.: Skin lesion classification from dermatoscopic images using deep learning techniques.
  Google Scholar

Maia L. et al.: Evaluation of melanoma diagnosis using deep features, 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP), 1–4, 2018.
DOI: https://doi.org/10.1109/IWSSIP.2018.8439373   Google Scholar

Marchetti M. A. et al.: Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol 78/2018, 270–277.
DOI: https://doi.org/10.1016/j.jaad.2017.08.016   Google Scholar

Marchetti M.A. et al: Computer algorithms show potential for improving dermatologists’ accuracy to diagnose cutaneous melanoma: results of the international skin imaging collaboration 2017. J Am Acad Dermatol 82/2020, 622–627.
DOI: https://doi.org/10.1016/j.jaad.2019.07.016   Google Scholar

Maron R.C. et al.: Systematic outperformance of 112 dermato-logists in multiclass skin cancer image classification by convo-lutional neural networks. Eur J Cancer 119/2019, 57–65.
  Google Scholar

Menzies S. et al.: Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Archives of Dermatology 132/1996, 1178–1182.
DOI: https://doi.org/10.1001/archderm.132.10.1178   Google Scholar

Murphree D. H. et al.: Deep learning for dermatologists: Part I. J Am Acad Dermatol 2020, 1–9.
  Google Scholar

Nida N. et al.: Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. International Journal of Medical Informatics 124/2019, 37–48.
DOI: https://doi.org/10.1016/j.ijmedinf.2019.01.005   Google Scholar

Nijeweme-d’Hollosy W. et al.: Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. Int. J. Med. Inform. 110/2018, 31–41.
DOI: https://doi.org/10.1016/j.ijmedinf.2017.11.010   Google Scholar

Phillips M. et al.: Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw Open 2/2019, 1913436.
DOI: https://doi.org/10.1001/jamanetworkopen.2019.13436   Google Scholar

Rosendahl C., Cameron A., McColl I., Wilkinson I.: Dermatoscopy in routine practice, Chaos and Clues. Australian Family Physician 41(7)/2012.
  Google Scholar

Simonyan K., Zisserman A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
  Google Scholar

Szegedy C. et al.: Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition 2015, 1–9.
DOI: https://doi.org/10.1109/CVPR.2015.7298594   Google Scholar

Tschandl P et al.: Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, webbased, international, diagnostic study. Lancet Oncol 2019(20)/2019, 938–947.
DOI: https://doi.org/10.1016/S1470-2045(19)30333-X   Google Scholar

Wang Y. et al.: Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection. Computers in Biology and Medicine 137/2021, 104812.
DOI: https://doi.org/10.1016/j.compbiomed.2021.104812   Google Scholar

Young A. T. et al.: Artificial intelligence in dermatology: A Primer. Journal of Investigative Dermatology 140/2020, 1504–1512.
DOI: https://doi.org/10.1016/j.jid.2020.02.026   Google Scholar

Yu L. et al.: Automated melano-ma recognition in dermoscopy images via very deep residual networks. IEEE transactions on medical imaging 36(4)/2017, 994–1004.
DOI: https://doi.org/10.1109/TMI.2016.2642839   Google Scholar

Zhang J. et al.: Skin lesion classification in dermoscopy images using synergic deep learning, Springer Nature Switzerland, LNCS 11071/2018, 12–20.
DOI: https://doi.org/10.1007/978-3-030-00934-2_2   Google Scholar

Zhang X.: Melanoma segmentation based on deep learning. Computer Assisted Surgery 22/2017, 267–277.
DOI: https://doi.org/10.1080/24699322.2017.1389405   Google Scholar

Download


Published
2021-12-20

Cited by

Michalska, M. (2021). SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 11(4), 18–21. https://doi.org/10.35784/iapgos.2804

Authors

Magdalena Michalska 
mmagamichalska@gmail.com
Lublin University of Technology, Department of Electronics and Information Technology Poland
http://orcid.org/0000-0002-0874-3285

Statistics

Abstract views: 307
PDF downloads: 191


Most read articles by the same author(s)

1 2 > >>